{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Regression\n",
    "Implementation of http://scikit-learn.org/stable/auto_examples/tree/plot_tree_regression.html#sphx-glr-auto-examples-tree-plot-tree-regression-py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.5])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "X = [[0 , 0], [2 , 2]]\n",
    "y = [0.5, 2.5]\n",
    "clf = tree.DecisionTreeRegressor()\n",
    "clf = clf.fit(X,y)\n",
    "clf.predict([[1, 6]])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Automatically created module for IPython interactive environment\n"
     ]
    },
    {
     "data": {
      "image/png": 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pp3jllVd46qmnABg4cCDXXnstH3zwAe3bt6datWrs3buXcuWyb0sPPPAAkZGR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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13c413968d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(__doc__)\n",
    "# Import the necessary modules and libraries \n",
    "import numpy as np\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#Create a random dataset\n",
    "rng = np.random.RandomState(1)\n",
    "X = np.sort(5 * rng.rand(80, 1), axis = 0)\n",
    "y = np.sin(X).ravel()\n",
    "y[::5] += 3 * (0.5 - rng.rand(16))\n",
    "\n",
    "# Fit regression model\n",
    "regr_1 = DecisionTreeRegressor(max_depth=2);\n",
    "regr_2 = DecisionTreeRegressor(max_depth=5);\n",
    "regr_1.fit(X, y);\n",
    "regr_2.fit(X, y);\n",
    "\n",
    "# Predict\n",
    "X_test = np.arange(0.0, 5.0, 0.01)[:, np.newaxis]\n",
    "y_1 = regr_1.predict(X_test)\n",
    "y_2 = regr_2.predict(X_test)\n",
    "\n",
    "# Plot the results\n",
    "plt.figure()\n",
    "plt.scatter(X, y, c=\"darkorange\", label=\"data\")\n",
    "plt.plot(X_test, y_1, color=\"cornflowerblue\", label=\"max_depth=2\", linewidth=2)\n",
    "plt.plot(X_test, y_2, color=\"yellowgreen\", label=\"max_depth=5\", linewidth=2)\n",
    "plt.xlabel(\"data\")\n",
    "plt.ylabel(\"target\")\n",
    "plt.title(\"Decision Tree Regression\")\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
